Solar-array modelling and maximum power point tracking using neural networks

This paper is studying a solar-array modelling and maximum power point tracking by comparing 2 neural networks which are back-propagation neural network and radial basis function neural network. Neural network has the potential to provide an improved method of deriving nonlinear models which is complementary to conventional techniques. The performance of the models and predicted maximum power point of solar cell are evaluated by comparing it with that of the conventional model by simulation. The simulation results has shown that both neural network work very well. In addition, the simulation results have shown that training for back-propagation takes longer time than radial basis function. However, back-propagation neural network needs less information for training. Radial basis function needs more information in order to get accurate modelling.